6 accepted papers
We propose a novel method for learning time-varying graphs from time-series data by leveraging techniques from directed acyclic graph (DAG) learning. The unknown graphs are parameterized with a time-varying structural vector autoregression model (SVAR), which we view as a linear structural equation…
We introduce SpinSVAR, a novel method for estimating a (linear) structural vector autoregression (SVAR) from time-series data under a sparse input assumption. Unlike prior approaches using Gaussian noise, we model the input as independent and identically distributed (i.i.d.) Laplacian variables, enf
In this paper we port assumptions and techniques from DAG (directed acyclic graph) learning and causal inference to time-series graph data. In particular, we view such data as indexed by a DAG obtained by unrolling the graph in time and generated by a causal linear structural equation model (SEM) fr…
We present a novel perspective and algorithm for learning directed acyclic graphs (DAGs) from data generated by a linear structural equation model (SEM). First, we show that a linear SEM can be viewed as a linear transform that, in prior work, computes the data from a dense input vector of random va…
In this work we theoretically contribute to neural network approximation by providing a novel tropical geometrical viewpoint to structured neural network compression. In particular, we show that the approximation error between two neural networks with ReLU activations and one hidden layer depends on…
Graph signal processing on directed graphs poses theoretical challenges since an eigendecomposition of filters is in general not available. Instead, Fourier analysis requires a Jordan decomposition and the frequency response is given by the Jordan normal form, whose computation is numerically unstab…